Overview

Dataset statistics

Number of variables13
Number of observations6321
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory642.1 KiB
Average record size in memory104.0 B

Variable types

Numeric9
Categorical4

Alerts

Bidder_ID has a high cardinality: 1054 distinct values High cardinality
Bidding_Ratio is highly correlated with Successive_Outbidding and 2 other fieldsHigh correlation
Last_Bidding is highly correlated with Early_BiddingHigh correlation
Auction_Bids is highly correlated with Starting_Price_AverageHigh correlation
Starting_Price_Average is highly correlated with Auction_BidsHigh correlation
Early_Bidding is highly correlated with Last_BiddingHigh correlation
Winning_Ratio is highly correlated with Bidding_Ratio and 1 other fieldsHigh correlation
Successive_Outbidding is highly correlated with Bidding_Ratio and 1 other fieldsHigh correlation
Class is highly correlated with Bidding_Ratio and 2 other fieldsHigh correlation
Record_ID has unique values Unique
Bidder_Tendency has 153 (2.4%) zeros Zeros
Auction_Bids has 2823 (44.7%) zeros Zeros
Starting_Price_Average has 3253 (51.5%) zeros Zeros
Winning_Ratio has 3640 (57.6%) zeros Zeros

Reproduction

Analysis started2022-12-12 08:18:18.853482
Analysis finished2022-12-12 08:18:33.501555
Duration14.65 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Record_ID
Real number (ℝ≥0)

UNIQUE

Distinct6321
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7535.829457
Minimum1
Maximum15144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.5 KiB
2022-12-12T08:18:33.597311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile741
Q13778
median7591
Q311277
95-th percentile14376
Maximum15144
Range15143
Interquartile range (IQR)7499

Descriptive statistics

Standard deviation4364.759137
Coefficient of variation (CV)0.5792008911
Kurtosis-1.194715162
Mean7535.829457
Median Absolute Deviation (MAD)3744
Skewness0.005441796984
Sum47633978
Variance19051122.33
MonotonicityStrictly increasing
2022-12-12T08:18:33.729481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
100151
 
< 0.1%
100131
 
< 0.1%
100111
 
< 0.1%
100091
 
< 0.1%
100051
 
< 0.1%
100041
 
< 0.1%
100031
 
< 0.1%
100021
 
< 0.1%
100011
 
< 0.1%
Other values (6311)6311
99.8%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
81
< 0.1%
101
< 0.1%
121
< 0.1%
131
< 0.1%
271
< 0.1%
ValueCountFrequency (%)
151441
< 0.1%
151391
< 0.1%
151381
< 0.1%
151371
< 0.1%
151291
< 0.1%
151281
< 0.1%
151241
< 0.1%
151231
< 0.1%
151211
< 0.1%
151151
< 0.1%

Auction_ID
Real number (ℝ≥0)

Distinct807
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1241.38823
Minimum5
Maximum2538
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.5 KiB
2022-12-12T08:18:33.868153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile121
Q1589
median1246
Q31867
95-th percentile2434
Maximum2538
Range2533
Interquartile range (IQR)1278

Descriptive statistics

Standard deviation735.770789
Coefficient of variation (CV)0.5926999881
Kurtosis-1.203514078
Mean1241.38823
Median Absolute Deviation (MAD)646
Skewness0.04581737075
Sum7846815
Variance541358.654
MonotonicityNot monotonic
2022-12-12T08:18:34.009517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58926
 
0.4%
187226
 
0.4%
25624
 
0.4%
65824
 
0.4%
249823
 
0.4%
3223
 
0.4%
162323
 
0.4%
186322
 
0.3%
111922
 
0.3%
86022
 
0.3%
Other values (797)6086
96.3%
ValueCountFrequency (%)
59
 
0.1%
69
 
0.1%
83
 
< 0.1%
99
 
0.1%
1711
0.2%
236
 
0.1%
258
 
0.1%
305
 
0.1%
318
 
0.1%
3223
0.4%
ValueCountFrequency (%)
25383
 
< 0.1%
25367
0.1%
25346
0.1%
253114
0.2%
25304
 
0.1%
25293
 
< 0.1%
252710
0.2%
251611
0.2%
251211
0.2%
25084
 
0.1%

Bidder_ID
Categorical

HIGH CARDINALITY

Distinct1054
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size49.5 KiB
a***a
 
112
n***t
 
85
e***e
 
67
i***a
 
50
r***r
 
49
Other values (1049)
5958 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters31605
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)4.4%

Sample

1st row_***i
2nd rowg***r
3rd rowt***p
4th row7***n
5th rowz***z

Common Values

ValueCountFrequency (%)
a***a112
 
1.8%
n***t85
 
1.3%
e***e67
 
1.1%
i***a50
 
0.8%
r***r49
 
0.8%
l***l48
 
0.8%
o***o45
 
0.7%
i***i44
 
0.7%
r***h43
 
0.7%
r***a41
 
0.6%
Other values (1044)5737
90.8%

Length

2022-12-12T08:18:34.137637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a***a112
 
1.8%
n***t85
 
1.3%
e***e67
 
1.1%
i***a50
 
0.8%
r***r49
 
0.8%
l***l48
 
0.8%
o***o45
 
0.7%
i***i44
 
0.7%
a43
 
0.7%
r***h43
 
0.7%
Other values (955)5735
90.7%

Most occurring characters

ValueCountFrequency (%)
*18972
60.0%
a1288
 
4.1%
e810
 
2.6%
i746
 
2.4%
r739
 
2.3%
n675
 
2.1%
o644
 
2.0%
l582
 
1.8%
s575
 
1.8%
t537
 
1.7%
Other values (31)6037
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation19027
60.2%
Lowercase Letter10318
32.6%
Decimal Number1891
 
6.0%
Connector Punctuation196
 
0.6%
Dash Punctuation172
 
0.5%
Currency Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1288
 
12.5%
e810
 
7.9%
i746
 
7.2%
r739
 
7.2%
n675
 
6.5%
o644
 
6.2%
l582
 
5.6%
s575
 
5.6%
t537
 
5.2%
m431
 
4.2%
Other values (16)3291
31.9%
Decimal Number
ValueCountFrequency (%)
0442
23.4%
1306
16.2%
2222
11.7%
8159
 
8.4%
9152
 
8.0%
7149
 
7.9%
4129
 
6.8%
6117
 
6.2%
3114
 
6.0%
5101
 
5.3%
Other Punctuation
ValueCountFrequency (%)
*18972
99.7%
.55
 
0.3%
Connector Punctuation
ValueCountFrequency (%)
_196
100.0%
Dash Punctuation
ValueCountFrequency (%)
-172
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21287
67.4%
Latin10318
32.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1288
 
12.5%
e810
 
7.9%
i746
 
7.2%
r739
 
7.2%
n675
 
6.5%
o644
 
6.2%
l582
 
5.6%
s575
 
5.6%
t537
 
5.2%
m431
 
4.2%
Other values (16)3291
31.9%
Common
ValueCountFrequency (%)
*18972
89.1%
0442
 
2.1%
1306
 
1.4%
2222
 
1.0%
_196
 
0.9%
-172
 
0.8%
8159
 
0.7%
9152
 
0.7%
7149
 
0.7%
4129
 
0.6%
Other values (5)388
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII31605
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
*18972
60.0%
a1288
 
4.1%
e810
 
2.6%
i746
 
2.4%
r739
 
2.3%
n675
 
2.1%
o644
 
2.0%
l582
 
1.8%
s575
 
1.8%
t537
 
1.7%
Other values (31)6037
 
19.1%

Bidder_Tendency
Real number (ℝ≥0)

ZEROS

Distinct489
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1425407372
Minimum0
Maximum1
Zeros153
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size49.5 KiB
2022-12-12T08:18:34.252182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008196721
Q10.027027027
median0.0625
Q30.166666667
95-th percentile0.545454545
Maximum1
Range1
Interquartile range (IQR)0.13963964

Descriptive statistics

Standard deviation0.1970835553
Coefficient of variation (CV)1.382647229
Kurtosis6.833835322
Mean0.1425407372
Median Absolute Deviation (MAD)0.046106557
Skewness2.531213439
Sum901.0000001
Variance0.03884192777
MonotonicityNot monotonic
2022-12-12T08:18:34.383371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25190
 
3.0%
0.2186
 
2.9%
0.5162
 
2.6%
0153
 
2.4%
0.333333333142
 
2.2%
0.1131
 
2.1%
0.111111111124
 
2.0%
1123
 
1.9%
0.090909091120
 
1.9%
0.142857143116
 
1.8%
Other values (479)4874
77.1%
ValueCountFrequency (%)
0153
2.4%
0.00302114853
 
0.8%
0.00604229614
 
0.2%
0.00680272119
 
0.3%
0.00724637745
 
0.7%
0.00819672146
 
0.7%
0.00826446318
 
0.3%
0.00840336114
 
0.2%
0.00869565241
 
0.6%
0.0090634449
 
0.1%
ValueCountFrequency (%)
1123
1.9%
0.9545454551
 
< 0.1%
0.951
 
< 0.1%
0.9444444441
 
< 0.1%
0.9285714291
 
< 0.1%
0.9166666672
 
< 0.1%
0.9130434781
 
< 0.1%
0.9090909091
 
< 0.1%
0.92
 
< 0.1%
0.8965517241
 
< 0.1%

Bidding_Ratio
Real number (ℝ≥0)

HIGH CORRELATION

Distinct400
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1276696725
Minimum0.011764706
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.5 KiB
2022-12-12T08:18:34.514321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.011764706
5-th percentile0.020833333
Q10.043478261
median0.083333333
Q30.166666667
95-th percentile0.4
Maximum1
Range0.988235294
Interquartile range (IQR)0.123188406

Descriptive statistics

Standard deviation0.1315304218
Coefficient of variation (CV)1.030240144
Kurtosis7.350693708
Mean0.1276696725
Median Absolute Deviation (MAD)0.046296296
Skewness2.421639032
Sum807.0000002
Variance0.01730025187
MonotonicityNot monotonic
2022-12-12T08:18:34.641169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.166666667233
 
3.7%
0.2211
 
3.3%
0.083333333189
 
3.0%
0.125180
 
2.8%
0.111111111175
 
2.8%
0.090909091168
 
2.7%
0.142857143159
 
2.5%
0.071428571152
 
2.4%
0.1150
 
2.4%
0.058823529144
 
2.3%
Other values (390)4560
72.1%
ValueCountFrequency (%)
0.01176470629
0.5%
0.0126582286
 
0.1%
0.0131578957
 
0.1%
0.01538461512
0.2%
0.01587301618
0.3%
0.01612903220
0.3%
0.0163934436
 
0.1%
0.01694915311
 
0.2%
0.0172413797
 
0.1%
0.0175438624
0.4%
ValueCountFrequency (%)
15
0.1%
0.9615384621
 
< 0.1%
0.8863636361
 
< 0.1%
0.8752
 
< 0.1%
0.8571428573
< 0.1%
0.8333333333
< 0.1%
0.8251
 
< 0.1%
0.8181818181
 
< 0.1%
0.809523811
 
< 0.1%
0.86
0.1%

Successive_Outbidding
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.5 KiB
0.0
5478 
1.0
 
469
0.5
 
374

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18963
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05478
86.7%
1.0469
 
7.4%
0.5374
 
5.9%

Length

2022-12-12T08:18:34.770563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T08:18:35.030817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.05478
86.7%
1.0469
 
7.4%
0.5374
 
5.9%

Most occurring characters

ValueCountFrequency (%)
011799
62.2%
.6321
33.3%
1469
 
2.5%
5374
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12642
66.7%
Other Punctuation6321
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011799
93.3%
1469
 
3.7%
5374
 
3.0%
Other Punctuation
ValueCountFrequency (%)
.6321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common18963
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011799
62.2%
.6321
33.3%
1469
 
2.5%
5374
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011799
62.2%
.6321
33.3%
1469
 
2.5%
5374
 
2.0%

Last_Bidding
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5807
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.463119377
Minimum0
Maximum0.999900463
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.5 KiB
2022-12-12T08:18:35.131459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.62963 × 10-5
Q10.0479282407
median0.4409375
Q30.8603626543
95-th percentile0.9901342593
Maximum0.999900463
Range0.999900463
Interquartile range (IQR)0.8124344136

Descriptive statistics

Standard deviation0.3800972294
Coefficient of variation (CV)0.8207327274
Kurtosis-1.608880478
Mean0.463119377
Median Absolute Deviation (MAD)0.4035119048
Skewness0.08771046524
Sum2927.377582
Variance0.1444739038
MonotonicityNot monotonic
2022-12-12T08:18:35.268687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.31481 × 10-529
 
0.5%
1.15741 × 10-525
 
0.4%
9.9206 × 10-619
 
0.3%
8.2672 × 10-617
 
0.3%
8.10185 × 10-517
 
0.3%
3.47222 × 10-516
 
0.3%
4.62963 × 10-515
 
0.2%
4.9603 × 10-615
 
0.2%
6.9444 × 10-613
 
0.2%
1.38889 × 10-512
 
0.2%
Other values (5797)6143
97.2%
ValueCountFrequency (%)
08
0.1%
1.6534 × 10-66
 
0.1%
2.3148 × 10-64
 
0.1%
3.3069 × 10-612
0.2%
3.4722 × 10-62
 
< 0.1%
3.858 × 10-64
 
0.1%
4.6296 × 10-69
0.1%
4.9603 × 10-615
0.2%
6.6138 × 10-67
0.1%
6.9444 × 10-613
0.2%
ValueCountFrequency (%)
0.9999004631
< 0.1%
0.99988425931
< 0.1%
0.99983961641
< 0.1%
0.99982407411
< 0.1%
0.99981977511
< 0.1%
0.99980820111
< 0.1%
0.99969907411
< 0.1%
0.99960152121
< 0.1%
0.99958217591
< 0.1%
0.99956018521
< 0.1%

Auction_Bids
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2316057484
Minimum0
Maximum0.788235294
Zeros2823
Zeros (%)44.7%
Negative0
Negative (%)0.0%
Memory size49.5 KiB
2022-12-12T08:18:35.415221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.142857143
Q30.454545455
95-th percentile0.678571429
Maximum0.788235294
Range0.788235294
Interquartile range (IQR)0.454545455

Descriptive statistics

Standard deviation0.2552520147
Coefficient of variation (CV)1.102097061
Kurtosis-1.156312208
Mean0.2316057484
Median Absolute Deviation (MAD)0.142857143
Skewness0.5872723251
Sum1463.979936
Variance0.06515359099
MonotonicityNot monotonic
2022-12-12T08:18:35.549204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
02823
44.7%
0.142857143208
 
3.3%
0.25166
 
2.6%
0.419354839166
 
2.6%
0.28158
 
2.5%
0.052631579149
 
2.4%
0.333333333143
 
2.3%
0.1143
 
2.3%
0.307692308127
 
2.0%
0.217391304122
 
1.9%
Other values (39)2116
33.5%
ValueCountFrequency (%)
02823
44.7%
0.052631579149
 
2.4%
0.1143
 
2.3%
0.142857143208
 
3.3%
0.181818182112
 
1.8%
0.217391304122
 
1.9%
0.25166
 
2.6%
0.28158
 
2.5%
0.307692308127
 
2.0%
0.333333333143
 
2.3%
ValueCountFrequency (%)
0.78823529468
1.1%
0.77215189916
 
0.3%
0.76315789517
 
0.3%
0.72307692324
 
0.4%
0.71428571434
0.5%
0.70967741949
0.8%
0.70491803313
 
0.2%
0.69491525417
 
0.3%
0.68965517216
 
0.3%
0.68421052657
0.9%

Starting_Price_Average
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4728213414
Minimum0
Maximum0.999935281
Zeros3253
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size49.5 KiB
2022-12-12T08:18:35.670568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.993592814
95-th percentile0.999935281
Maximum0.999935281
Range0.999935281
Interquartile range (IQR)0.993592814

Descriptive statistics

Standard deviation0.4899121676
Coefficient of variation (CV)1.036146478
Kurtosis-1.973158938
Mean0.4728213414
Median Absolute Deviation (MAD)0
Skewness0.08641184491
Sum2988.703699
Variance0.240013932
MonotonicityNot monotonic
2022-12-12T08:18:35.772768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
03253
51.5%
0.9935928141921
30.4%
0.999935281345
 
5.5%
0.993528095322
 
5.1%
0.93534567289
 
1.4%
0.67640476448
 
0.8%
0.9993528143
 
0.7%
0.99889977636
 
0.6%
0.9871209134
 
0.5%
0.82525857329
 
0.5%
Other values (12)201
 
3.2%
ValueCountFrequency (%)
03253
51.5%
0.51460714722
 
0.3%
0.5469666715
 
0.2%
0.55305026115
 
0.2%
0.67640476448
 
0.8%
0.67646948324
 
0.4%
0.8058428595
 
0.1%
0.82525857329
 
0.5%
0.9295856773
 
< 0.1%
0.93528095326
 
0.4%
ValueCountFrequency (%)
0.999935281345
 
5.5%
0.9993528143
 
0.7%
0.99889977636
 
0.6%
0.99514607113
 
0.2%
0.9935928141921
30.4%
0.993528095322
 
5.1%
0.9871209134
 
0.5%
0.96770519511
 
0.2%
0.96764047629
 
0.5%
0.96123329123
 
0.4%

Early_Bidding
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5690
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4306825513
Minimum0
Maximum0.999900463
Zeros15
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size49.5 KiB
2022-12-12T08:18:35.894903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.31481 × 10-5
Q10.0266203704
median0.3601041667
Q30.8267607564
95-th percentile0.9886998457
Maximum0.999900463
Range0.999900463
Interquartile range (IQR)0.800140386

Descriptive statistics

Standard deviation0.3807851666
Coefficient of variation (CV)0.8841434729
Kurtosis-1.582106675
Mean0.4306825513
Median Absolute Deviation (MAD)0.354728836
Skewness0.2204189586
Sum2722.344407
Variance0.1449973431
MonotonicityNot monotonic
2022-12-12T08:18:36.026675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.31481 × 10-542
 
0.7%
1.15741 × 10-530
 
0.5%
9.9206 × 10-625
 
0.4%
3.47222 × 10-525
 
0.4%
4.62963 × 10-521
 
0.3%
4.9603 × 10-620
 
0.3%
3.3069 × 10-618
 
0.3%
8.2672 × 10-618
 
0.3%
6.94444 × 10-515
 
0.2%
6.9444 × 10-615
 
0.2%
Other values (5680)6092
96.4%
ValueCountFrequency (%)
015
0.2%
1.6534 × 10-612
0.2%
2.3148 × 10-66
 
0.1%
3.3069 × 10-618
0.3%
3.4722 × 10-62
 
< 0.1%
3.858 × 10-69
0.1%
4.6296 × 10-610
0.2%
4.9603 × 10-620
0.3%
6.6138 × 10-614
0.2%
6.9444 × 10-615
0.2%
ValueCountFrequency (%)
0.9999004631
< 0.1%
0.99986882721
< 0.1%
0.99983961641
< 0.1%
0.99979629631
< 0.1%
0.99969907411
< 0.1%
0.99960152121
< 0.1%
0.99956018521
< 0.1%
0.99953009261
< 0.1%
0.99949074071
< 0.1%
0.99947916671
< 0.1%

Winning_Ratio
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct72
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3677311985
Minimum0
Maximum1
Zeros3640
Zeros (%)57.6%
Negative0
Negative (%)0.0%
Memory size49.5 KiB
2022-12-12T08:18:36.163299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.851851852
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.851851852

Descriptive statistics

Standard deviation0.4365734667
Coefficient of variation (CV)1.187208125
Kurtosis-1.737443619
Mean0.3677311985
Median Absolute Deviation (MAD)0
Skewness0.4041892864
Sum2324.428906
Variance0.1905963918
MonotonicityNot monotonic
2022-12-12T08:18:36.296417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03640
57.6%
1826
 
13.1%
0.75143
 
2.3%
0.833333333110
 
1.7%
0.666666667107
 
1.7%
0.878
 
1.2%
0.85714285774
 
1.2%
0.91666666769
 
1.1%
0.562
 
1.0%
0.86666666758
 
0.9%
Other values (62)1154
 
18.3%
ValueCountFrequency (%)
03640
57.6%
0.33333333312
 
0.2%
0.43
 
< 0.1%
0.562
 
1.0%
0.5555555569
 
0.1%
0.57142857112
 
0.2%
0.621
 
0.3%
0.61111111111
 
0.2%
0.6153846153
 
< 0.1%
0.6252
 
< 0.1%
ValueCountFrequency (%)
1826
13.1%
0.9767441868
 
0.1%
0.95454545512
 
0.2%
0.955
 
0.1%
0.94736842111
 
0.2%
0.94444444411
 
0.2%
0.9411764719
 
0.1%
0.93756
 
0.1%
0.93548387110
 
0.2%
0.93333333340
 
0.6%

Auction_Duration
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.5 KiB
7
2427 
3
1408 
1
1289 
5
1060 
10
 
137

Length

Max length2
Median length1
Mean length1.021673786
Min length1

Characters and Unicode

Total characters6458
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row7

Common Values

ValueCountFrequency (%)
72427
38.4%
31408
22.3%
11289
20.4%
51060
16.8%
10137
 
2.2%

Length

2022-12-12T08:18:36.415933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T08:18:36.520246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
72427
38.4%
31408
22.3%
11289
20.4%
51060
16.8%
10137
 
2.2%

Most occurring characters

ValueCountFrequency (%)
72427
37.6%
11426
22.1%
31408
21.8%
51060
16.4%
0137
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6458
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
72427
37.6%
11426
22.1%
31408
21.8%
51060
16.4%
0137
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common6458
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
72427
37.6%
11426
22.1%
31408
21.8%
51060
16.4%
0137
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72427
37.6%
11426
22.1%
31408
21.8%
51060
16.4%
0137
 
2.1%

Class
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.5 KiB
0
5646 
1
675 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6321
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05646
89.3%
1675
 
10.7%

Length

2022-12-12T08:18:36.615926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-12T08:18:36.716151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
05646
89.3%
1675
 
10.7%

Most occurring characters

ValueCountFrequency (%)
05646
89.3%
1675
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6321
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05646
89.3%
1675
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common6321
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05646
89.3%
1675
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05646
89.3%
1675
 
10.7%

Interactions

2022-12-12T08:18:31.899738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:23.715748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:24.829771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:25.985473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:26.946822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:27.905965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:29.065968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.031349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.951643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:32.159564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:23.857408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:24.948001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:26.092162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:27.051637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:28.012980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:29.176360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.136534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:31.056153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:32.269483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:23.974140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:25.047768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:26.190268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:27.150140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:28.130772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:29.280723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.235614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:31.153537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:32.384270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:24.095748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:25.153490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:26.293242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:27.259292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:28.243466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:29.386553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.336361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:31.254414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:32.492715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:24.215645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:25.267369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:26.395827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:27.371448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:28.356974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:29.493431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.438093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:31.354003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:32.593529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:24.336819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:25.384458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:26.498676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:27.476920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:28.472468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:29.597881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.539464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:31.453465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:32.703840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:24.465422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:25.673128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:26.616327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:27.591364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:28.594134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:29.711965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.649516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:31.563084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:32.843078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:24.581065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:25.782605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:26.723848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:27.696503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:28.706142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:29.819403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.751076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:31.668625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:32.962373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:24.702710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:25.886079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:26.834064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:27.802241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:28.963848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:29.925837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:30.851297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-12T08:18:31.783007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-12-12T08:18:36.799381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-12-12T08:18:36.972207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-12T08:18:37.146938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-12T08:18:37.321491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-12T08:18:37.638994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-12T08:18:37.753423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-12T08:18:33.140944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-12T08:18:33.377740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Record_IDAuction_IDBidder_IDBidder_TendencyBidding_RatioSuccessive_OutbiddingLast_BiddingAuction_BidsStarting_Price_AverageEarly_BiddingWinning_RatioAuction_DurationClass
01732_***i0.2000000.4000000.00.0000280.0000000.9935930.0000280.66666750
12732g***r0.0243900.2000000.00.0131230.0000000.9935930.0131230.94444450
23732t***p0.1428570.2000000.00.0030420.0000000.9935930.0030421.00000050
347327***n0.1000000.2000000.00.0974770.0000000.9935930.0974771.00000050
45900z***z0.0512820.2222220.00.0013180.0000000.0000000.0012420.50000070
58900i***e0.0384620.1111110.00.0168440.0000000.0000000.0168440.80000070
610900m***p0.4000000.2222220.00.0067810.0000000.0000000.0067740.75000070
712900k***a0.1379310.4444441.00.7680440.0000000.0000000.0163111.00000071
8132370g***r0.1219510.1851851.00.0350210.3333330.9935280.0239630.94444471
927600e***t0.1551720.3461540.50.5709940.3076920.9935930.4137880.61111171

Last rows

Record_IDAuction_IDBidder_IDBidder_TendencyBidding_RatioSuccessive_OutbiddingLast_BiddingAuction_BidsStarting_Price_AverageEarly_BiddingWinning_RatioAuction_DurationClass
631115115760s***l0.0285710.0400000.00.0572030.2800000.9935930.0572030.00000030
631215121927v***-0.1818180.0952380.00.9556940.1428570.0000000.9553130.00000010
631315123760c***e0.1111110.0400000.00.5697260.2800000.9935930.5697260.00000030
631415124760q***00.6250000.2000000.50.4730630.2800000.9935930.4319020.50000030
631515128760i***l0.0222220.0400000.00.6296060.2800000.9935930.6296060.00000030
631615129760l***t0.3333330.1600001.00.7385570.2800000.9935930.6863580.88888931
6317151372481s***s0.0306120.1304350.00.0057540.2173910.9935930.0000100.87878870
6318151382481h***t0.0555560.0434780.00.0156630.2173910.9935930.0156630.00000070
6319151392481d***d0.0769230.0869570.00.0686940.2173910.9935930.0004150.00000070
6320151442481a***l0.0163930.0434780.00.3403510.2173910.9935930.3403510.00000070